Processing facilities are often managed using process control systems. Example processing facilities include manufacturing plants, chemical plants, crude oil refineries, ore processing plants, and paper or pulp manufacturing and processing plants. Among other operations, process control systems typically manage the use of motors, valves, and other industrial equipment in the processing facilities.
In conventional process control systems, controllers are often used to control the operation of the industrial equipment in the processing facilities. The controllers could, for example, monitor the operation of the industrial equipment, provide control signals to the industrial equipment, and generate alarms when malfunctions are detected.
Conventional controllers typically operate using models of the industrial processes being controlled. The models allow the controllers to determine, based on input signals from sensors or other data, how to control the industrial equipment. For long-term and successful control of an industrial process, it is often necessary to perform process identification and generate an accurate model of the process. The quality of the model can dramatically impact the performance of the controller that uses the model.
Process identification is often one of the most important aspects of any control project and can easily consume large amounts of project resources. However, identification may require direct interaction with the actual industrial process, and personnel are often less than enthusiastic about this aspect of a project. As such, it is common for controller performance to degrade over time since model enhancement is usually not performed after the initial model is generated. Various techniques have been developed for performing process identification, although each technique typically has various drawbacks or weaknesses.